TY - GEN
T1 - Towards Robust Basal Ganglia Segmentation in Ultra-Low-Field Pediatric MRI via an Optimized MS-TCNet
AU - Liu, Yi
AU - Zhu, Yueyue
AU - Jiang, Haotian
AU - Bai, Xiaoyu
AU - Cai, Rongqing
AU - Chen, Geng
N1 - Publisher Copyright:
© The Author(s) 2026.
PY - 2026
Y1 - 2026
N2 - Magnetic Resonance Imaging (MRI) provides a non-invasive means to examine pediatric brain anatomy. However, in low-resource settings, ultra-low-field scanners are more widely used due to their affordability and portability, but they often generate images with a low signal-to-noise ratio and poor tissue contrast. This severely hampers the accurate delineation of critical subcortical structures such as the basal ganglia. In this work, we adapt and refine the multi-scale Transformer – CNN network (MS-TCNet) for bilateral basal ganglia segmentation in 0.064T pediatric MRI. Our optimized version, OMS-TCNet, integrates improved data augmentation and fine-tuned training configurations. Extensive experiments on the challenge dataset demonstrate that our method achieves robust and reliable segmentation performance under ultra-low-field imaging conditions. The code is publicly available at: https://github.com/Onion-Boy/OMS-TCNet.
AB - Magnetic Resonance Imaging (MRI) provides a non-invasive means to examine pediatric brain anatomy. However, in low-resource settings, ultra-low-field scanners are more widely used due to their affordability and portability, but they often generate images with a low signal-to-noise ratio and poor tissue contrast. This severely hampers the accurate delineation of critical subcortical structures such as the basal ganglia. In this work, we adapt and refine the multi-scale Transformer – CNN network (MS-TCNet) for bilateral basal ganglia segmentation in 0.064T pediatric MRI. Our optimized version, OMS-TCNet, integrates improved data augmentation and fine-tuned training configurations. Extensive experiments on the challenge dataset demonstrate that our method achieves robust and reliable segmentation performance under ultra-low-field imaging conditions. The code is publicly available at: https://github.com/Onion-Boy/OMS-TCNet.
KW - Basal Ganglia Segmentation
KW - Low-Field MRI
KW - Pediatric Brain
UR - https://www.scopus.com/pages/publications/105031054144
U2 - 10.1007/978-3-032-14417-1_3
DO - 10.1007/978-3-032-14417-1_3
M3 - 会议稿件
AN - SCOPUS:105031054144
SN - 9783032144164
T3 - Lecture Notes in Computer Science
SP - 29
EP - 38
BT - Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance - Second MICCAI Challenge, LISA 2025, Held in Conjunction with MICCAI 2025, Proceedings
A2 - Lepore, Natasha
A2 - Linguraru, Marius George
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2025, held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2025
Y2 - 27 September 2025 through 27 September 2025
ER -